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GPU Computing Gems Jade EditionChapter 3: Optimizing Parallel Prefix Operations for the Fermi Architecture
3.1 INTRODUCTION TO PARALLEL PREFIX OPERATIONS
Scan (also known as parallel prefix sums), is a fundamental parallel building block that can form the basis of many efficient parallel algorithms, including sorting, computational geometry algorithms such as quickhull, and graph algorithms such as minimum spanning tree, to name just a few . Given an input sequence of values
and an arbitrary binary associative operator that we denote ?, the scan operation produces the output sequence
This scan is inclusive because each element si has accumulated values from all elements aj where j = i. A related operation is the exclusive scan, where only elements with j < i are accumulated:
The exclusive scan operator requires an additional prefix value p, which is often assumed to be the identity value for the operator .
As a concrete example, applying inclusive scan with the + operator to the input sequence [3 1 7 0 4 1 6 3] produces the result [3 4 11 11 15 16 22 25]; applying exclusive scan with prefix p = 0 to the same input produces the result [0 3 4 11 11 15 16 22].
In this chapter we focus on developing efficient intra-thread-block scan implementations, which are valuable in applications in which scan is used as an operator on local data. When the data are already in registers or on-chip memory, scan is generally instruction limited. A number of recent papers have explored the efficient implementation of global parallel prefix operations in CUDA [2-8]. In most cases, the operators used are simple arithmetic operations, such as integer or floating-point addition. These operators generally map to a single hardware instruction, and therefore the amount of work performed per element of the array is minimal. If the input and output arrays reside in external memory, the cost of transporting the data from external memory to on-chip registers and back dwarfs the cost of computation, and therefore the goal of most efficient GPU scan implementations has been to saturate the available memory bandwidth.
In a number of applications, the input elements are single bits with a value of zero or one. Alterna- tively, we can consider them to be Boolean predicates. There are two common operations on sequences of elements with corresponding Boolean true/false predicates: stream compaction (also known as fil- ter), and split. Stream compaction removes all elements in the input stream that have false flags . Stream compaction can be used, for example, to take a sequence of object collision test results and generate a sequence of pairs of colliding objects for computing collision response in a physics sim- ulation. The split operation moves all elements with true flags to the beginning of the sequence, and all elements with false flags to the end. We can construct a radix sort for keys of arbitrary length by using the split operation on the sequence of keys by each of their bits in sequence from least to most significant bit [9, 10].
Because of the importance of binary scans, the architects of the NVIDIA Fermi GPU added some new operations for improving their efficiency. These operations allow the threads of a CUDA warp to compute cooperatively. The first is a new _ballot() intrinsic function for collecting "votes" from a warp of threads, and the second is a new scalar intrinsic called _popc() (short for "population count"). Using these new instructions we can construct efficient binary scans that execute fewer instructions and require much less shared memory than equivalent code that uses full 32-bit operations.
3.1.1 Intra-Warp Scan
Efficient scan implementations on NVIDIA GPUs frequently take advantage of the fact that the graph- ical processing unit (GPU) executes parallel threads in groups of 32 called warps. The threads of a warp execute in an Single Instruction, Multiple Data (SIMD) fashion, under which they collectively execute each instruction. The CUDA C code in Listing 3.1 shows how one might implement a parallel intra-warp scan primitive.
This function is tailored to operate within a warp in three ways. First, it assumes that the number of threads in a warp is 32, which limits the number of parallel steps to five. Second, because it assumes that the threads of a warp execute synchronously, it omits syncthreads() that would otherwise be necessary when using shared memory. Finally, by padding the intermediate storage for each warp with zeros, it ensures legal indexing of the shared memory array without conditionals (note that this requires extra shared memory). The s data variable is assumed to be a pointer to shared memory, and it must be declared volatile as shown to ensure that the stores to shared memory are executed; otherwise, in the absence of barrier synchronization, the compiler may incorrectly choose to keep intermediate values in registers. Because s data is declared as volatile, it must be reread from shared memory on every reference; therefore, a separate local variable t is required to keep the running sum in a register throughout the scan.
3.2 Efficient Binary Prefix Operations on Fermi
A central goal of CUDA is to enable cooperation among the threads in a block; this cooperation is very powerful because it enables collective parallel operations that are not bottlenecked by off-chip memory accesses. GPUs with Compute Capability 1.0 implemented the simple _syncthreads() barrier synchronization to allow threads within a block to cooperate via shared memory. Compute Capability 1.2 added a warp "vote" instruction that enabled two new intrinsics, _any() and _all(). Each of these functions takes a predicate1 as input from each thread that calls it. _any() returns true to the calling thread if any thread in the same warp passes a true predicate, and _all() returns true only if all threads in the warp pass a true predicate. These intrinsics allow programs to make simple collective decisions within each warp.
Compute Capability 2.0, implemented by the Fermi GPU architecture, provides new instructions that enable more efficient implementation of reduction and scan operations on binary or Boolean inputs. We focus on the case where the operator in question is addition and the values held by each thread are predicates.
1 We use the term predicate interchangeably for values which are (a) a single bit, (b) Boolean true/false values, or (c) integers that are either zero or non-zero.
3.2.1 Primitives Provided by Compute Capability 2.0
The CUDA C compiler provides a number of useful integer arithmetic functions that are supported on all CUDA devices (Appendix C.2.3, ). We use the following intrinsic functions to optimize parallel prefix operations.
On previous generations of NVIDIA processors, these intrinsic functions mapped to instruction sequences that are tens of instructions long. However, NVIDIA GPUs based on the Fermi architec- ture (Compute Capability 2.0) provide much more efficient support. In particular, the _popc() and _clz() intrinsics compile to a single machine instruction.
In addition to providing more efficient support for these integer arithmetic operations, Compute Capability 2.0 GPUs also introduce the capability to efficiently collect predicate values across parallel threads. The first such primitive that we use operates at the warp level.
This primitive is similar in spirit to the existing _any() and _all() warp vote functions. However, rather than computing a cumulative result, it returns the "ballot" of predicates provided by each thread in the warp. _ballot() provides a mechanism for quickly broadcasting one bit per thread among all the threads of a warp without using shared memory.
Finally, the Fermi architecture provides a new class of barrier intrinsics that simultaneously syn- chronize all the threads of a thread block and perform a reduction across a per-thread predicate. The three barrier intrinsics are:
Every thread of the block provides a predicate value as it arrives at the barrier. When the barrier returns, the prescribed value is returned to every thread.
3.2.2 Implementing Binary Prefix Sums with Fermi Intrinsics
In this section we demonstrate the implementation and performance of efficient binary scans and segmented scans using the Compute Capability 2.0 intrinsics described in Section 3.2.1.
126.96.36.199 Intra-Warp Binary Prefix Sums
Consider the case in which every thread of a warp holds a single predicate. By combining the _ballot() and _popc() intrinsics, we can easily count the number of true values within the warp using the code shown in Listing 3.2.
We call this a binary reduction because the value held by each thread can be represented with a single bit. When the threads of a warp call this function, they each pass a single bool flag as an argument to the function. The _ballot() intrinsic packs the predicates from all threads in a warp into a single 32-bit integer and returns this integer to every thread. Each thread then individually counts the number of bits that are set in the 32-bit ballot using _popc(). This accomplishes a parallel reduction across all threads of the warp.
To compute the prefix sums of predicates across a warp requires only a small modification. Every thread receives the same 32-bit ballot containing the predicate bits from all threads in its warp, but it only counts the predicates from threads that have a lower index. The key is to construct a separate bit mask for each lane of the warp, as illustrated by the code in Listing 3.3.
For each lane k, lanemask_lt computes a 32-bit mask whose bits are 1 in positions less than k and 0 everywhere else. To compute the prefix sums, each thread applies its mask to the 32-bit ballot, and then counts the remaining bits using _popc(). By modifying the construction of the mask, we can construct the four fundamental prefix sums operations:
1. Forward inclusive scan: set all bits less than or equal to the current lane. (lanemask_le)
2. Forward exclusive scan: set all bits less than the current lane. (lanemask_lt)
3. Reverse inclusive scan: set all bits greater than or equal to the current lane. (lanemask_ge)
4. Reverse exclusive scan: set all bits greater than the current lane. (lanemask_gt)
Fermi hardware computes these lane masks internally and provides instructions for reading them, but doing so requires the use of inline PTX assembly. The lanemask lt function in Listing 3.4 produces a lane mask in a single instruction, compared with four for the function in Listing 3.3. The PTX version is also more robust, because it is guaranteed to work correctly for multidimensional thread blocks. The source code accompanying this book includes a header that defines all four versions of lanemask.
We can extend the basic binary intra-warp scan (or reduction) technique to scans of multibit integer sequences by performing prefix sums one bit at a time and adding the individual results scaled by the appropriate power of two. For full 32-bit integers, the generic intra-warp scan shown in Listing 3.1 will outperform this approach. The bitwise scan requires one step per bit, but computes 32 results per step, whereas the generic intra-warp scan uses 32-bit arithmetic, but requires five steps to generate 32 results. The cost of one step of bitwise scan is four instructions, and for the generic scan it is three instructions. Clearly the generic scan will be faster for integers with more than about 4 bits, and our experiments confirm this.
188.8.131.52 Intra-Warp Segmented Binary Prefix Sums
Segmented scan generalizes scan by simultaneously performing separate parallel scans on arbitrary contiguous segments of the input sequence. For example, applying inclusive scan with the + operator to a sequence of integer sequences gives the following result:
Segmented scans are useful in mapping irregular computations such as quicksort and sparse matrix- vector multiplication onto regular parallel execution.
Segmented sequences augment the sequence of input values with a segment descriptor that encodes how the sequence is partitioned. A common segment descriptor representation is a head flags array that stores 1 for each element that begins a segment and 0 for all others. The head flags representation for the preceding example sequence is:
The intra-warp binary scan technique of Section 3.2.2 can be extended to binary segmented scan as demonstrated by the code in Listing 3.5. In addition to the predicate p, each thread passes a 32-bit hd value that contains head flags for its entire warp.(2) _clz() is used to count the number of thread lanes greater than or equal to the thread's segment, and the count is used to compute a segment mask that is applied to the ballot along with the lane mask.
184.108.40.206 Intra-Block Binary Reductions and Prefix Sums
In CUDA programs, we often need to perform reductions and parallel prefix sums across entire thread blocks. Counting true predicates (a binary reduction) across an entire block simply requires a single call to _syncthreads_count(). We compared binary count using _syncthreads_count() to an efficient 32-bit parallel reduction . We ran the test on an NVIDIA GeForce GTX 480 GPU using CUDA 3.1 on 32-bit Windows XP, with 512-thread blocks and a wide range of grid sizes. The _syncthreads_count() version is on average two times faster, as shown in Figure 3.1, and requires only a single line of code, compared with about 20 lines of code for the 32-bit parallel reduction.(3) _syncthreads_count() also uses no shared memory, whereas a 32-bit reduction uses 128 bytes per warp. As in the warp-level case, we can also use _syncthreads_count() to sum multibit integers by performing one reduction for each bit of the values, then scaling and adding the results together appropriately.
(2) Note hd can be computed from per-thread head flags using _ballot().
(3) Example code is included on the accompanying CD.
In order to compute binary prefix sums across an entire thread block, we use warp-level prefix sums procedures as fundamental building blocks. We perform binary prefix sums within each warp independently, collecting the cumulative sums from each into shared memory. These partial sums are 5-bit integers, which we can combine with a single warp using either 5-bit binary prefix sums or generic 32-bit scan. As discussed in Section 3.2.2, for 5-bit integers, the generic 32-bit scan is slightly faster, so this is what we use in the code in Listing 3.6.
The main optimization that Fermi enables over previous architectures is the use of _ballot() and _popc() in the warp_prefix_sums() function. To measure the benefit of this optimization, we compared the code in Listing 3.6 to the same function modified to call warp_scan() rather than warp_prefix_sums() in step A. The relative performance of these two versions is shown in Figure 3.2 (The test configuration was the same as used for the binary reduction test of Figure 3.1.) The binary intra-block scan using hardware intrinsics is on average 24% faster than the 32-bit scan version, and the CUDA Visual Profiler shows that it executes about 31% fewer instructions. The binary scan with intrinsics also uses much less shared memory; only the single-warp call to warp_scan() in step C of Listing 3.6 requires shared memory, so at most 256 bytes of shared memory are needed per block, compared with 256 bytes per warp for a full 32-bit scan.
We have presented binary scan and reduction primitives that exploit new features of the Fermi archi- tecture to accelerate important parallel algorithm building blocks. Often in applications that apply prefix sums to large sequences, bandwidth is the primary bottleneck because data must be shared between thread blocks, requiring global memory traffic and separate kernel launches. We experimented with binary scan and reduction primitives in large binary prefix sums, stream compaction, and radix sort, with average speed-ups of 3%, 2%, and 4%, respectively. For intra-block radix sort, which is not dominated by intra-block communication through global memory, we observed speed-ups as high as 12%.
For applications that are less bandwidth bound, these primitives can help improve binary prefix sums performance by up to 24%, and binary reduction performance by up to 100%. Furthermore, the intrinsics often simplify code, and their lower shared memory usage may help improve GPU occupancy in kernels that combine prefix sums with other computation. Undoubtedly there are further uses for the new instructions provided by Fermi, and we hope that their demonstration here will help others to experiment.
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Wen-mei W. Hwu, Professor, University of Illinois
Mark Harris and Michael Garland
Related Keywords:NVIDIA Fermi GPU, parallel algorithm primitives, multithread programming, GPU programming